Development takes.. Fair amount of experts Fair amount of time Unfortunatly.. Life sciences is as flexible as life itself Maintaining and updating of systems takes Fair amount of experts Fair amount of time Therefore.. Life science data systems are created and kept running by large, dedicated teams

First, we initiated community consultation on phenotypic, high-throughput and locus specific data [3] in consultation with the Gen2Phen projects and representatives from P3G and the model organism community. We successfully tested this strategy in a series of systems: XGAP: an eXtensible Genotype And Phenotype data platform [5] to integrate systems genetics studies (GWAS, GWL) on gene expression, metabolomics and proteomics data. See http://www.xgap.org . MAGETAB-OM: a microarray experiment data platform based on the MAGE-TAB data format standard. See http://magetab-om.sourceforge.net/ Pheno-OM: a data platform to integrate phenotype data from locus specific annotations from LSDBs to rich clinical reports from cohort studies and model organism data. See http://wwwdev.ebi.ac.uk/microarray-srv/pheno/ FINDIS: a mutation database for monogenic diseases belonging to the Finnish disease heritage. See http://www.findis.org/molgenis_findis/ Each of the systems was created in a standard way by providing a MOLGENIS compatible model.

When looking in more detail at the spreadsheet-like data structure on the previous slide, you will notice it stores a combination of ‘Phenotype’ and ‘Individual’ just like the sheet with the eyes on it. We separate this ‘measured’ data from the annotations about these individuals and phenotypes because we consider the annotations to be constant, whereas measurements can or will always vary.